In the last 10 years, evolutionary algorithms (EA) have been occasionally used as tools to help parameterizing computational models of the brain. As models grow more and more in complexity, manually adjusting parameters become unreasonable, while automatic approaches, like EA, can provide acceptable solutions.

Modern EA can also help computational neuroscience beyond optimization: the use of multiple-­‐objectives EA (MOEA) allows to find multiple trade-­‐off solutions to the studied problem, revealing intrinsic properties of the problem itself, and thus actively participating in the investigation process. Modern generative EA can also be used to fully generate the structure of brain networks models based on known anatomical and electrophysiological data and thus directly participate in the model design.

The goal of this workshop is to gather neuroscientists around the current use of EA in computational neuroscience, to advertise the possibilities of this approach, as well as to discuss the emerging and future applications of EA in our field.